The new era of quantitative cell imaging-challenges and opportunities

Abstract

Quantitative optical microscopy-an emerging, transformative approach to single-cell biology-has seen dramatic methodological advancements over the past few years. However, its impact has been hampered by challenges in the areas of data generation, management, and analysis. Here we outline these technical and cultural challenges and provide our perspective on the trajectory of this field, ushering in a new era of quantitative, data-driven microscopy. We also contrast it to the three decades of enormous advances in the field of genomics that have significantly enhanced the reproducibility and wider adoption of a plethora of genomic approaches.

Figure 1.. Current image experiment to analysis pipelines have many bottlenecks that hinder making data and tools findable, accessible, interoperable, and reusable (FAIR)

This illustration highlights ways in which the community can build consensus, incentives, and use cases across the imaging pipeline. Information transfer, from data generation through publication, is shown with arrows. Addressing the list of features in filled circles will enable the iterative sharing of data, resources, and tools in robust and easy-to-access repositories, which is critical to the growth of the quantitative cell imaging community and closes the loop between analysis, generation, and discovery.

Figure 2.. Community-established standards enable findable, accessible, interoperable, and reusable (FAIR) resources

The consistent use of dynamic standards, benchmarks, and annotations for data generation and the sharing of these resources in repositories is critical to developing a quantitative cell imaging community. Supporting publicly available resources positions the community to build upon shared expertise and develop resources that will empower and engage a new wave of investigators and lower the barrier for productive collaborations.